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Perishable inventory control with backlogging penalties: A mixed-integer linear programming model via two-step approximation

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COMPUTERS & OPERATIONS RESEARCH 2025年 176卷

作者： Wu, Yulun Tanaka, Shunji Kyoto Univ Grad Sch Engn Dept Elect Engn Nishikyo Ku Kyoto 6158510 Japan Okayama Univ Fac Environm Life & Nat Sci & Technol 3-1-1 Tsushima NakaKita Ku Okayama 7008530 Japan

This study proposes a novel approximate mixed-integer linear programming (MILP) model for the perishable inventory control problem considering non-stationary demands and backlogging penalties. Because of the existence of the waste costs incurred by outdated products in the cost function, it is difficult to apply the linearization technique employed for the non-perishable inventory control problem directly to our problem. To address this difficulty, we develop a two-step approximation method. In the first step, we approximate each expected cost to simplify the cost function, making it easy to handle. In the second step, we apply an existing linearization technique to linearize this function and then obtain the MILP model. We evaluate the proposed model in computer simulations by comparing it with other existing methods. The results show that our model closely matches a benchmark method capable of obtaining near-optimal solutions in solution quality, and it achieves abetter trade-off between solution quality and computational efficiency than existing heuristics.

关键词： Inventory control Perishable products Non-stationary demands mixed-integer linear programming model Piecewise linearization

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Unmanned Aerial Vehicle Inspection Routing and Scheduling for Engineering Management

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Engineering 2024年第5期36卷 223-239页

作者： Lu Zhen Zhiyuan Yang Gilbert Laporte Wen Yi Tianyi Fan School of Management Shanghai UniversityShanghai 200444China Department of Decision Sciences HEC MontréalMontréalQC H3T 2A7Canada School of Management University of BathBath BA27AYUK Department of Building and Real Estate The Hong Kong Polytechnic UniversityHong Kong 999077China

Technological advancements in unmanned aerial vehicles(UAVs)have revolutionized various industries,enabling the widespread adoption of UAV-based *** engineering management,UAV-based inspection has emerged as a highly efficient method for identifying hidden risks in high-risk construction environments,surpassing traditional inspection *** on this foundation,this paper delves into the optimization of UAV inspection routing and scheduling,addressing the complexity introduced by factors such as no-fly zones,monitoring-interval time windows,and multiple monitoring *** tackle this challenging problem,we propose a mixed-integer linear programming(MILP)model that optimizes inspection task assignments,monitoring sequence schedules,and charging *** comprehensive consideration of these factors differentiates our problem from conventional vehicle routing problem(VRP),leading to a mathematically intractable model for commercial solvers in the case of large-scale *** overcome this limitation,we design a tailored variable neighborhood search(VNS)metaheuristic,customizing the algorithm to efficiently solve our *** numerical experiments are conducted to validate the efficacy of our proposed algorithm,demonstrating its scalability for both large-scale and real-scale *** experiments and a case study based on an actual engineering project are also conducted,providing valuable insights for engineering managers to enhance inspection work efficiency.

关键词： Engineering management Unmanned aerial vehicle Inspection routing and scheduling optimization mixed-integer linear programming model Variable neighborhood search metaheuristic

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Mathematical modeling and hybrid evolutionary algorithm to schedule flexible job shop with discrete operation sequence flexibility

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COMPUTERS & OPERATIONS RESEARCH 2025年 176卷

作者： Yuan, Shuai Zhu, Xiaomin Cai, Wei Gao, Jinsheng Zhang, Runtong Beijing Jiaotong Univ Sch Mech Elect & Control Engn Beijing Peoples R China North China Elect Power Univ Sch Econ & Management Beijing Peoples R China Beijing Jiaotong Univ Sch Econ & Management Beijing Peoples R China

In actual industrial production, several operations of a job may not have precedence relationships and can be placed at any point in the process route. However, traditional flexible job shop scheduling problems (FJSP) often assume that all operations of each job must be processed in strict linear order. Therefore, this research addresses the FJSP with discrete operation sequence flexibility (FJSPDS) with the objective of minimizing the makespan. Based on existing models, two novel mixed-integer linear programming (MILP) models are formulated by improving the description methods of variables and constraints, significantly enhancing the models' performance. Additionally, a hybrid evolutionary algorithm (HEA) is proposed to solve large-scale instances through the following three aspects. An improved encoding method is proposed, which makes the search space of the HEA and solution space of the problem more compatible and reduces the possibility of optimal solutions being missed. A special neighborhood structure is designed according to the characters of sequence-free operations, and an iterative local search method is introduced to improve the quality of the solution. A knowledge-driven reinitialization operator is developed, which generates new individuals based on the features of the historical elite population, guiding the evolution of populations, avoiding premature convergence while also avoiding falling into local optima. Finally, a total of 110 benchmark problem instances are utilized to verify the superior effectiveness of the MILP models and the HEA in solving FJSPDS.

关键词： Flexible job shop scheduling mixed-integer linear programming model Discrete operation sequence flexibility Hybrid evolutionary algorithm Knowledge-driven reinitialization Iterative local search

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Electric bus charging scheduling on a bus network

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2024年 161卷

作者： Zhou, Yu Meng, Qiang Ong, Ghim Ping Wang, Hua Beihang Univ Sch Transportat Sci & Engn Beijing 100191 Peoples R China Beihang Univ Sch Transportat Sci & Engn Key Lab Intelligent Transportat Technol & Syst Minist Educ Beijing 100191 Peoples R China Natl Univ Singapore Dept Civil & Environm Engn Singapore 117576 Singapore Hefei Univ Technol Sch Automot & Transportat Engn Hefei 230009 Peoples R China

This study addresses the electric bus charging scheduling problem (EBCSP) within a bus network comprising multiple bus routes, depots, and a fleet of heterogeneous electric buses (EBs) operated by a public transport (PT) operator. The EBCSP aims to minimize total costs by concurrently determining the assignment of EBs to trips(i.e., EB-to-trip assignment) and their corresponding charging schedules. A partial charging policy allows for flexible charging durations, considering a published timetable consisting of trip tasks. We formulate the EBCSP as a mixed-integer linear programming model and a set-covering formulation. For small-scale EBCSPs, we propose a branch-and-price algorithm utilizing the set-covering formulation for exact solutions. As large-scale EBCSPs pose computational challenges, we develop an optimizationbased adaptive large neighborhood search ( opt -ALNS) method. The opt -ALNS method employs ALNS operators for EB-to-trip assignment and solves the remaining linear programming problem efficiently. To enhance the opt -ALNS process, we introduce a labeling method to assess solution feasibility and rebuild solutions. Moreover, we incorporate EB battery degradation effects into the proposed method. Finally, we assess the performance of the opt -ALNS method on real-life instances and compare it with the branch-and-price algorithm.

关键词： Electric bus charging scheduling Bus network mixed-integer linear programming model Set-covering model Branch-and-price algorithm Adaptive large neighborhood search method Battery degradation

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Mathematical model and knowledge-based iterated greedy algorithm for distributed assembly hybrid flow shop scheduling problem with dual-resource constraints

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 239卷

作者： Yu, Fei Lu, Chao Zhou, Jiajun Yin, Lvjiang China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Hubei Univ Automot Technol Sch Econ & Management Shiyan 442002 Peoples R China

With the development of economic globalization, distributed hybrid flow shop scheduling problem (DHFSSP) has become prevalent in realistic manufacturing systems. Moreover, to accord with the actual production scenarios and satisfy the requirement of manufacturing market, it is imperative to comprehensively explore various complex manufacturing scenarios (e.g., production assembly) and production-constrained resources (e.g., worker resources) in DHFSSP. However, the integration mode of DHFSSP, assembly shop problem (ASP), and dual-resource constraints (DRC) has not been reported in existing literature. Thus, to fill out this research gap, this paper first attempts to investigate DAHFSSP-DRC with minimization the total tardiness (TTD). To solve this problem, a mixed-integer linear programming (MILP) model and a knowledge-based iterated greedy algorithm (KBIG) are presented. The novelties of KBIG are as follows: (1) An efficient decoding is developed to improve the solution's quality;(2) A knowledge-based NEH (KB-NEH) initialization strategy is presented to generate an initial solution;(3) A knowledge-based destruction and construction is designed to improve the exploration capability;(4) A product-based local search is proposed to enhance the exploitation capability. Additionally, to validate the proposed model, we implement CPLEX to solve it on 24 small-sized instances. To verify the effectiveness of the proposed KBIG, extensive experiments are conducted to compare with other 7 comparison algorithms on 405 large-sized instances. Experimental results illustrate that KBIG is superior to its competitors.

关键词： Distributed hybrid flow shop scheduling problem Assembly shop problem Dual-resource constraints Iterated greedy algorithm mixed-integer linear programming model

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Optimal Placement and Sizing of Distributed Generation in Electrical DC Distribution Networks Using a Stochastic mixed-integer LP model

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2025年第8期50卷 5835-5851页

作者： Valencia-Diaz, Alejandro Hincapie, Ricardo A. Gallego, Ramon A. Univ Tecnol Pereira Dept Elect Engn Pereira 660003 Risaralda Colombia

This paper presents a stochastic mixed-integer linear mathematical model for finding the optimal placement and sizing of distributed generation in a DC distribution network, considering the uncertainty of electrical demand and distributed renewable sources. The proposed model accurately represents the original mixed-integer nonlinear model, obtaining a globally optimal solution in less computational time with low errors. The mathematical model allows for considering constraints related to the maximum limits for the penetration of distributed generation, such as those specified by Resolution CREG 174 of 2021. Furthermore, the uncertainties of the electrical demand, wind energy-based distributed generation (DG), and solar energy-based DG are considered in the mathematical models using a two-stage stochastic programming approach. The accuracy and efficiency of the proposed model were tested and validated on a 21-node DC test system from the specialized literature, and the effectiveness and robustness were assessed on a 69-node DC test system. The obtained results show that the proposed stochastic mixed-integer linear mathematical model performs well.

关键词： Distributed generation DC distribution systems mixed-integer linear programming model Stochastic programming

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Optimal strategy to reduce energy waste in an electricity distribution network through direct/indirect bulk load control

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ENERGY 2024年 294卷

作者： Cerna, Fernando V. Dantas, Jamile T. Naderi, Ehsan Contreras, Javier Univ Fed Roraima Dept Elect Engn BR-69310000 Boa Vista Brazil Univ Fed Roraima Postgrad Program Nat Resources PRONAT Res Area Management & Dynam Nat Resources Renewabl Ave Nova Iorque Aeroporto BR-69310000 Boa Vista Brazil Arkansas State Univ Coll Engn & Comp Sci Dept Elect Engn Jonesboro AR 72401 USA Univ Castilla La Mancha Sch Ind Engn Ciudad Real 13071 Spain

In recent decades, the ever-increasing demand for electricity in large cities is leading the electricity distribution network (EDN) to an operational state of fatigue, especially at peak load times. In this context, load congestion, technical losses, and voltage deviations across the EDN can contribute to the high waste of electricity as well as to the worsening of the supply service. To face this problem, this paper proposes a mixed integer linear programming (MILP) model that aims to reshape the demand profile (i.e., load reduction in peak periods without load rebound in the remaining periods of the day) of total consumers while the voltage deviations and technical losses are minimized. In the proposed model, the demand profile reshaping is carried out through direct (DC) and indirect (IC) bulk control of residential, commercial, and industrial loads, while the load factor (LF), i.e., indicator related to the efficient use of electricity, is improved. Due to the scheduling of these loads at certain times of the day, the occurrence of voltage deviations and technical losses can be mitigated by the efficient control of capacitor banks (CBs). To corroborate the applicability of the proposed MILP model, the 33-node IEEE test system was used. The results show the technical and economic gains for the distribution company (DISCO) and the total number of consumers.

关键词： Capacitor banks Distribution company Electricity distribution network mixed-integer linear programming model Direct control Indirect control

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mixed-integer linear programming and composed heuristics for three-stage remanufacturing system scheduling problem

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年第PartB期137卷

作者： Wang, Wenjie Tian, Guangdong Zhang, Honghao Li, Zhiwu Lv, Lei Li, Cheng Zhengzhou Univ Sch Mech & Power Engn Zhengzhou 450001 Peoples R China Beijing Univ Civil Engn & Architecture Sch Machinery & Vehicles Beijing 100044 Peoples R China Shandong Univ Sch Mech Engn Jinan 250061 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Taipa 999078 Macao Peoples R China

The three-stage remanufacturing system scheduling problem (3T-RSSP) has been a hot research topic recently. The remanufacturing system in this paper is equipped with a novel configuration of unrelated parallel disassembly/reassembly workstations and parallel dedicated flow-shop-type reprocessing lines. To this end, a mixedinteger linear programming (MILP) model based on the adjacent sequence-based modeling idea is first proposed to address the 3T-RSSP for a makespan minimization. Compared with other ideas, the adjacent sequence-based modeling idea is effective in deciding precedence relationship between two adjacent operations, especially for the investigated 3T-RSSP. The 3T-RSSP is NP (non-deterministic polynomial)-hard, we also design 18 composed heuristics for large-sized problems to gain a better performance, compared to traditional isolated heuristics. Simulation experiments are carried out on a publicly available dataset to test the performance the MILP model and composed heuristics. Results imply that the MILP model solved by CPLEX can seek optimum solutions within a short time when the problem size is small. It is found that when problem size becomes 2.0, 4.0, 8.0 times large, performance indicators NCs (number of constraints) and NBVs (number of binary variables) of the model become 3.29, 11.81, 44.60 and 3.43, 12.57, 48.00 times large. Besides, compared with other composed heuristics, LTRTF (longest total reprocessing time-first available machine) gains the best performance. Instance P5-C3-D2/A2 is selected to quantitatively analyze the MILP model by presenting the detailed 0-1 binary variable values. Finally, by comparing with position-based MILP model, the adjacent sequence-based MILP model has better performance in characterizing the investigated 3T-RSSP.

关键词： Remanufacturing system scheduling Makespan mixed-integer linear programming model Exact algorithm Composed heuristics

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Exact formulation and two-stage optimisation method for corridor allocation problem consider separated man-vehicle logistics passage in manufacturing workshops

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COMPUTERS & INDUSTRIAL ENGINEERING 2024年 198卷

作者： Ji, Dan Zhang, Zeqiang Liu, Junqi Zhao, Minjie Liang, Wei Liu, Silu Southwest Jiaotong Univ Sch Mech Engn Chengdu 610031 Peoples R China Technol & Equipment Rail Transit Operat & Maintena Chengdu 610031 Peoples R China Southwest Jiaotong Univ Engn Trainning Ctr Chengdu 610031 Peoples R China

Passage is an essential logistics carrying structure in the layout, rational planning of its location can effectively improve production efficiency and safety of the workshop. In this paper, we introduce the concept of man-vehicle diversion and propose a novel corridor allocation problem with a separated man-vehicle logistics passage (CAP_SMVP). CAP_SMVP needs to sequence the facilities and determine the final exact location of the changeable sidewalk. Subsequently, we construct two mixed-integer linear programming models to minimise the material handling costs (MHC). The models for small-scale instances are solved precisely using GUROBI. We choose a two- stage method (combining meta-heuristic algorithm and linear programming, CMLP) to address large-scale problems. The correctness of the proposed model and CMLP is confirmed in solving the benchmark instances. Then, the calculation results of multiple algorithms for solving the initial CAP demonstrate the universality of the proposed algorithm. Finally, a real-world case is conducted, and managerial insights are provided. The comparison results show the proposed layout structure's advantages and highlight the efficiency of the two-stage method.

关键词： Workshop layout Corridor allocation problem Separated man-vehicle logistics passage mixed-integer linear programming model Two-stage method

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A Genetic algorithm scheme for large scale open-pit mine production scheduling

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MINING TECHNOLOGY-TRANSACTIONS OF THE INSTITUTIONS OF MINING AND METALLURGY 2023年第4期132卷 225-236页

作者： Azadi, Nooshin Mirzaei-Nasirabad, Hossein Mousavi, Amin Sahand Univ Technol Fac Min Engn Tabriz Iran Tarbiat Modares Univ Fac Min Engn Tehran Iran Sahand Univ Technol Tabriz Iran

Due to the large size of open-pit mines' long-term production scheduling (OPMPS) problem in large-scale deposits, it is challenging to solve that problem as the mixed integer linear programming (MILP) model. This study used an approach of the genetic algorithm (GA) to tackle this challenge. So, in a small hypothetical deposit, based on the blocks in the ultimate pit limit and scenarios with 2-6 phases, net present values (NPV) and computational times obtained from the GA and MILP model were compared to evaluate the GA. Also, the GA was applied to a large-scale deposit to determine the efficiency of the GA in real deposits. The maximum NPV was obtained for the four-phase scenario in the hypothetical deposit and the six-phase scenario in the large-scale deposit. Although the GA's NPV decreased slightly compared to the global optimum solution from the MILP model, the computational time was significantly reduced.

关键词： Open-Pit mine ultimate pit limit net present value operational constraints NP-hard problem long-term production scheduling genetic algorithm mixed-integer linear programming model

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